Opportunity number
NSF 18-539
National Science Foundation (NSF)
Critical Techniques Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA)
Due date
Big Data
Project funding
$200,000 to a maximum of $500,000 per year, for 3-4yrs of support. Minimum of $600,000 of total funding. Maximum award size: $2M of total NSF funding.
Program funding
Funding size
$5M to $25M Up to $5M

BIGDATA program

RFP Summary provided by the agency

The NSF research program on Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA) seeks to further develop data science, a transdisciplinary field of research, to understand phenomena through data analytics and massive computation on vast amounts of empirical data. This solicitation invites proposals that focus on the foundations of data science and innovative applications of data science. Of particular interest are proposals for which data science and the availability of big data are creating new opportunities for research not possible before, as well as proposals that explore research topics identified by participating NSF directorates listed in this solicitation. In addition, the cloud option for proposals, which was first introduced in the FY 2017 BIGDATA solicitation, is continued in this solicitation through the partnership of NSF with AWS, Google, IBM, and Microsoft Azure. If additional cloud providers join the program, resources/credits from those providers will be made available under the same terms and conditions as described in this solicitation. The BIGDATA program webpage (https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504767) provides the current list of cloud partners participating in the program. The BIGDATA program is a component of NSF’s Harnessing the Data Revolution for 21st-Century Science and Engineering Big Idea. Additional cloud credits/resources will be provided by AWS, Google, IBM, and Microsoft.

What is the mission and focus of the program: research, social, economic or others?

Some of the participating NSF directorates/divisions have provided research themes and areas of interest.
MPS/DMS is interested in foundational mathematical and statistical approaches to big data challenges generated by complex dependence structures, missing information, sparsity, and heterogeneous data. Proposals must describe the specific big data challenges being addressed and justify why the proposed research cannot be considered in one of the “core” DMS programs.
The Directorate for Education and Human Resources (EHR) is interested in fostering novel, transformative, multidisciplinary approaches that address the use of large data sets and/or learning analytics software to create actionable knowledge for improving STEM teaching and learning environments (formal and informal) in the medium term, and to revolutionize learning in the longer term.
The Directorate for Social, Behavioral and Economic Sciences (SBE) is interested in research that advances computational social science and analytic methods using social network, sensor, text, video, administrative, and other big data. Example topics include algorithms for social policy choices; cognitive assistance; promoting American competitiveness and economic opportunity, health, and well-being in different regions and populations; new forms of work; financial markets and critical institutions of society; and the intersection of law, governance, and data science.
The Directorate for Engineering (ENG) is interested in topics specified by the programs belonging to the Divisions of Civil, Mechanical, and Manufacturing Innovation (CMMI); Chemical, Bioengineering, Environmental, and Transport Systems (CBET); and Electrical, Communications, and Cyber Systems (ECCS). Engineering-specific applications, however, must incorporate aspects of data science, data analytics, or innovation in data-driven discovery at a level significantly beyond the scope of existing ENG programs.

How do you submit to this opportunity?

Proposers may opt to submit proposals in response to this Program Solicitation via Grants.gov or via the NSF FastLane system. see: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf18001

Who are the target applicants: cities, universities, companies, small business, nonprofits, or others?

“Institutions of Higher Education (IHEs) – Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.
Non-profit, non-academic organizations: Independent museums, observatories, research labs, professional societies and similar organizations in the U.S. associated with educational or research activities.”

Example project(s) summaries from past RFPs:

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802284&HistoricalAwards=false; BIGDATA: IA: Collaborative Research: Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media. September 20, 2017. Amount Awarded: $400,000. The project investigates the use of big-data analysis techniques for classifying crisis-related data in social media with respect to situational awareness categories, such as caution, advice, fatality, injury, and support, with the goal of helping emergency response teams identify useful information. A major challenge is the scale of the data, where millions of short messages are continuously posted during a disaster, and need to be analyzed. The use of current technologies based on automated machine learning is limited due to the lack of labeled data for an emergent target disaster, and the fact that every event is unique in terms of geography, culture, infrastructure, technology, and the people involved. To tackle the above challenges, domain adaptation techniques that make use of existing labeled data from prior disasters and unlabeled data from a current disaster are designed. The resulting models are continuously updated and improved based on feedback from crowdsourcing volunteers. The research will provide real, usable solutions to emergency response organizations and will enable these organizations to improve the speed, quality and efficiency of their response.

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